Information processing device, information processing method, and program

ABSTRACT

In order to more accurately categorize a relation between a plurality of entities, an information processing apparatus ( 1 ) includes a relation vector generation section ( 11 ), a feature vector generation section ( 12 ), and a relation categorization section ( 13 ). The relation vector generation section ( 11 ) generates a relation vector representing a relation between a plurality of entities of interest from at least one sentence which has been selected from a sentence set and in which the plurality of entities of interest occur. The feature vector generation section ( 12 ) generates, for each entity of interest, a feature vector representing a feature of that entity of interest from at least one sentence which has been selected from the sentence set and in which that entity of interest occurs. The relation categorization section ( 13 ) categorizes a relation between the plurality of entities of interest with use of a relation vector and feature vectors.

TECHNICAL FIELD

The present invention relates to a technique for categorizing a relationbetween a plurality of entities.

BACKGROUND ART

A technique for categorizing a relation between a plurality of entitiesis known. For example, Non-Patent Literature 1 discloses a relatedtechnique for categorizing a relation between a pair of entities basedon similarity of sentence expressions describing relations between thepair of entities. This related technique discriminates, in a sentence inwhich a certain pair of entities occurs, sentence expressions describingrelations between the certain pair with use of a syntactic structure ofsentence. Moreover, the related technique determines whether or not arelation between one pair is identical with a relation between anotherpair depending on whether or not discriminated sentence expressions aresimilar to each other.

CITATION LIST Non-Patent Literature

-   [Non-patent Literature 1]-   Yuan, Chenhan, et al. “Clustering-based Unsupervised Generative    Relation Extraction.” arXiv preprint arXiv: 2009.12681 (2020).

SUMMARY OF INVENTION Technical Problem

In the related technique disclosed in Non-Patent Literature 1, there isroom for improvement in accuracy of categorizing a relation between aplurality of entities. The reason for this is as follows.

Here, a relation between a plurality of entities may be determined inaccordance with features of the respective entities. For example, arelation “X is a leader of Y” for entities X and Y holds true when X isa person rather than an animal. However, the related techniquecategorizes a relation of a pair based on similarity of sentenceexpressions which have been discriminated using a syntactic structure ofsentence. Therefore, there are cases in which a relation in accordancewith features of respective entities cannot be categorized.

An example aspect of the present invention is accomplished in view ofthe above problem, and its example object is to provide a technique formore accurately categorizing a relation between a plurality of entities.

Solution to Problem

An information processing apparatus according to an example aspect ofthe present invention includes: a relation vector generation means ofgenerating a relation vector that represents a relation between aplurality of entities of interest from at least one relation vectorgeneration sentence which has been selected from a sentence set and inwhich the plurality of entities of interest occur; a feature vectorgeneration means of generating, for each of the plurality of entities ofinterest, a feature vector that represents a feature of that entity ofinterest from at least one feature vector generation sentence which hasbeen selected from the sentence set and in which that entity of interestoccurs; and a relation categorization means of categorizing a relationbetween the plurality of entities of interest with use of a relationvector which has been generated by the relation vector generation meansand feature vectors which have been generated by the feature vectorgeneration means.

An information processing method according to an example aspect of thepresent invention includes: generating a relation vector that representsa relation between a plurality of entities of interest from at least onerelation vector generation sentence which has been selected from asentence set and in which the plurality of entities of interest occur;generating, for each of the plurality of entities of interest, a featurevector that represents a feature of that entity of interest from atleast one feature vector generation sentence which has been selectedfrom the sentence set and in which that entity of interest occurs; andcategorizing a relation between the plurality of entities of interestwith use of the relation vector and the feature vectors which have beenrespectively generated for the plurality of entities of interest.

A program according to an example aspect of the present invention is aprogram for causing a computer to function as an information processingapparatus, the program causing the computer to function as: a relationvector generation means of generating a relation vector that representsa relation between a plurality of entities of interest from at least onerelation vector generation sentence which has been selected from asentence set and in which the plurality of entities of interest occur; afeature vector generation means of generating, for each of the pluralityof entities of interest, a feature vector that represents a feature ofthat entity of interest from at least one feature vector generationsentence which has been selected from the sentence set and in which thatentity of interest occurs; and a relation categorization means ofcategorizing a relation between the plurality of entities of interestwith use of a relation vector which has been generated by the relationvector generation means and feature vectors which have been generated bythe feature vector generation means.

An information processing apparatus according to an example aspect ofthe present invention includes: a relation vector generation means ofgenerating a relation vector with use of an algorithm including aplurality of parameters from at least one relation vector generationsentence which has been selected from a sentence set and in which aplurality of entities of interest occur, the relation vectorrepresenting a relation between the plurality of entities of interest; arelation vector generation parameter updating means of updating theplurality of parameters such that a degree of similarity increasesbetween a plurality of relation vectors that are generated by therelation vector generation means from a plurality of sentences in all ofwhich the plurality of entities of interest occur; and a relationcategorization means of categorizing a relation between the plurality ofentities of interest with use of a relation vector which has beengenerated by the relation vector generation means.

An information processing method according to an example aspect of thepresent invention includes: generating a relation vector with use of analgorithm including a plurality of parameters from at least one relationvector generation sentence which has been selected from a sentence setand in which a plurality of entities of interest occur, the relationvector representing a relation between the plurality of entities ofinterest; updating the plurality of parameters such that a degree ofsimilarity increases between a plurality of relation vectors that aregenerated by the relation vector generation means from a plurality ofsentences in all of which the plurality of entities of interest occur;and categorizing a relation between the plurality of entities ofinterest with use of a relation vector which has been generated by therelation vector generation means.

A program according to an example aspect of the present invention is aprogram for causing a computer to function as an information processingapparatus, the program causing the computer to function as: a relationvector generation means of generating a relation vector with use of analgorithm including a plurality of parameters from at least one relationvector generation sentence which has been selected from a sentence setand in which a plurality of entities of interest occur, the relationvector representing a relation between the plurality of entities ofinterest; a relation vector generation parameter updating means ofupdating the plurality of parameters such that a degree of similarityincreases between a plurality of relation vectors that are generated bythe relation vector generation means from a plurality of sentences inall of which the plurality of entities of interest occur; and a relationcategorization means of categorizing a relation between the plurality ofentities of interest with use of a relation vector which has beengenerated by the relation vector generation means.

Advantageous Effects of Invention

According to an example aspect of the present invention, it is possibleto more accurately categorize a relation between a plurality ofentities.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a configuration of an informationprocessing apparatus according to a first example embodiment of thepresent invention.

FIG. 2 is a flowchart illustrating a flow of an information processingmethod according to the first example embodiment of the presentinvention.

FIG. 3 is a block diagram illustrating a configuration of an informationprocessing apparatus according to a second example embodiment of thepresent invention.

FIG. 4 is a flowchart illustrating a flow of an information processingmethod according to the second example embodiment of the presentinvention.

FIG. 5 is a block diagram illustrating a configuration of an informationprocessing apparatus according to a third example embodiment of thepresent invention.

FIG. 6 is a flowchart illustrating a flow of an information processingmethod according to the third example embodiment of the presentinvention.

FIG. 7 is a block diagram illustrating a configuration of an informationprocessing apparatus according to a fourth example embodiment of thepresent invention.

FIG. 8 is a flowchart illustrating a flow of an information processingmethod according to the fourth example embodiment of the presentinvention.

FIG. 9 is a diagram for comparing a categorization result in Example ofthe present invention with a categorization result in ComparativeExample.

FIG. 10 is a block diagram illustrating a configuration of aninformation processing apparatus according to a fifth example embodimentof the present invention.

FIG. 11 is a flowchart illustrating a flow of an information processingmethod according to the fifth example embodiment of the presentinvention.

FIG. 12 is a block diagram illustrating an example of a hardwareconfiguration of the information processing apparatus according to eachof the example embodiments of the present invention.

EXAMPLE EMBODIMENTS Term Definitions

Before describing each of example embodiments of the present invention,terms used in each of the example embodiments will be described. Notethat an information processing apparatus according to each of theexample embodiments is an apparatus for categorizing a relation betweena plurality of entities of interest with reference to a sentence set inwhich the plurality of entities of interest occur.

(Entity)

Entities are elements constituting an event that is expressed by asentence. Each entity is distinguished from other entities by its name.An entity can be a tangible object or can be an intangible object. Anentity can be a subject or an object expressed by a noun, can be anaction or a relation expressed by a verb, or can be a state or a degreeexpressed by an adjective or adjectival verb. There are types ofentities. For example, a type of an entity whose name is “Japan” is“Country”. A type of an entity whose name is “Shinzo Abe” is “Person”. Atype of an entity whose name is “Blue” is “Color”. Hereinafter, when itis necessary to separately describe entities, descriptions will be madewith reference signs e1, e2, and so forth.

(Plurality of Entities of Interest)

A plurality of entities of interest are a plurality of entities ofinterest among entities which occur in a sentence set. In the presentexample embodiment, it is assumed that the number of entities ofinterest is two. Note, however, that the number of entities of interestis not limited to two, and may be three or more.

(Sentence Set)

A sentence set is a set of sentences. A sentence set includes a sentencein which at least one of or all of a plurality of entities of interestoccur. A sentence consists of one or more words.

(Occurrence)

A case in which an entity occurs in a sentence means that the entity isreferred to in that sentence. Moreover, a case in which an entity isreferred to in a sentence means that one or more words constituting thesentence represent that entity. In other words, a sentence in which anentity occurs includes a word representing that entity. Note that a wordrepresenting a certain entity is not limited to one. For example, oneexample of a word representing an entity “Shinzo Abe” can be a pluralityof words “Former Prime Minister Abe”, “Abe Shinzo”, and the like. A wordindicating an entity can also be considered as a type of wordrepresenting that entity. For example, a pronoun “he” indicating anentity “Shinzo Abe” can also be regarded as a word representing theentity “Shinzo Abe”. For example, in a case where a sentence includeswords “Former Prime Minister Abe”, another sentence includes words “AbeShinzo”, and still another sentence includes a word “he” indicating“Shinzo Abe”, the entity “Shinzo Abe” occurs in all of these sentences.

(Relation)

In an event that is represented by a sentence, a plurality of entitiesmay have relevance. For example, entities X “Japan” and Y “Shinzo Abe”have a relationship in which “X was a leader of Y”. Such a relationshipbetween entities is referred to as a relation.

First Example Embodiment

The following description will discuss a first example embodiment of thepresent invention in detail with reference to the drawings. The presentexample embodiment is a basic form of example embodiments describedlater.

<Configuration of Information Processing Apparatus>

The following description will discuss an information processingapparatus 1 according to the first example embodiment of the presentinvention with reference to FIG. 1 . FIG. 1 is a block diagramillustrating the configuration of the information processing apparatus1.

The information processing apparatus 1 includes a relation vectorgeneration section 11, a feature vector generation section 12, and arelation categorization section 13. The relation vector generationsection 11 is an example configuration for realizing the “relationvector generation means” recited in claims. The relation categorizationsection 13 is an example configuration for realizing the “relationcategorization means” recited in claims. An example configuration forrealizing the “relation categorization means” recited in claims.

(Relation Vector Generation Section)

The relation vector generation section 11 generates a relation vectorrepresenting a relation between a plurality of entities of interest fromat least one relation vector generation sentence which has been selectedfrom a sentence set.

For example, the relation vector generation section 11 generates arelation vector as follows. Specifically, (1) first, the relation vectorgeneration section 11 converts a relation vector generation sentenceinto a word sequence or into a graph in which words serve as nodes. Therelation vector generation section 11 may use, when carrying out thisconversion, information obtained by parsing the relation vectorgeneration sentence. (2) Next, the relation vector generation section 11converts each of words included in the word sequence or the graph whichhas been generated in (1) into a vector as a word vector. For example,the relation vector generation section 11 uses a one-hot-vector as aword vector corresponding to a word. In this one-hot-vector, elements ofeach vector correspond to different types of words, respectively, andonly an element corresponding to each word is 1, and other elements are0. (3) Next, the relation vector generation section 11 calculates arelation vector with use of the word vectors of the respective wordswhich have been generated in (2). For example, the relation vectorgeneration section 11 calculates a relation vector by inputting wordvectors of respective words into a calculation model in which a processthat reflects a structure of a word sequence or a graph is carried out.Examples of such a calculation model include, but not limited to, arecurrent neural network, a graph neural network, Transformer, and thelike.

Note that, as a technique for the relation vector generation section 11to generate a relation vector, it is possible to employ, for example, atechnique disclosed in Non-Patent Literature 1 above, ReferenceLiterature 1 below, or Reference Literature 2 below.

[Reference Literature 1] Zeng, Daojian, et al. “Distant supervision forrelation extraction via piecewise convolutional neural networks.”Proceedings of the 2015 conference on empirical methods in naturallanguage processing. 2015

[Reference Literature 2] Miwa, Makoto, and Mohit Bansal. “End-to-EndRelation Extraction using LSTMs on Sequences and Tree Structures.”Proceedings of the 54th Annual Meeting of the Association forComputational Linguistics (Volume 1: Long Papers). 2016.

In each of the techniques disclosed in Non-Patent Literature 1,Reference Literature 1 below, and Reference Literature 2 below, arelation between entities is classified with use of a classificationmodel. The relation vector generation section 11 may use any of thesetechniques and use, as a relation vector, a vector which is input into aclassification model.

(Relation Vector Generation Sentence)

A relation vector generation sentence is a sentence which has beenselected from a sentence set and in which a plurality of entities ofinterest occur. In a case where entities e1 and e2 are entities ofinterest among entities which occur in a sentence set, the sentence setincludes n relation vector generation sentences in which the entities e1and e2 of interest occur. Hereinafter, a reference sign S(e1,e2)i isgiven to each of the n relation vector generation sentences. Note that nis an integer of 1 or more, and i is an integer of 1 or more and n orless.

(Feature Vector Generation Section)

The feature vector generation section 12 generates, for each of aplurality of entities of interest, a feature vector representing afeature of that entity of interest from at least one feature vectorgeneration sentence in which that entity of interest occurs. The featurevector represents, for example, a type of entity of interest which isinferred from a sentence in which that entity of interest occurs.

For example, the feature vector generation section 12 generates afeature vector as follows. Specifically, (1) first, the feature vectorgeneration section 12 converts a feature vector generation sentence intoa word sequence or into a graph in which words serve as nodes. Thefeature vector generation section 12 may use, when carrying out thisconversion, information obtained by parsing the feature vectorgeneration sentence. (2) Next, the feature vector generation section 12converts each of words included in the word sequence or the graph whichhas been generated in (1) into a vector as a word vector. For example,the feature vector generation section 12 uses a one-hot-vector as a wordvector corresponding to a word. In this one-hot-vector, elements of eachvector correspond to different types of words, respectively, and only anelement corresponding to each word is 1, and other elements are 0. (3)Next, the feature vector generation section 12 calculates a featurevector with use of the word vectors of the respective words which havebeen generated in (2). For example, the feature vector generationsection 12 calculates a feature vector by inputting word vectors ofrespective words into a calculation model in which a process thatreflects a structure of a word sequence or a graph is carried out.Examples of such a calculation model include, but not limited to, arecurrent neural network, a graph neural network, Transformer, and thelike. As a technique for generating a feature vector from a featurevector generation sentence, for example, Word2Vec, a known techniquedisclosed in Reference Literature 3 below, or the like can be applied.

[Reference Literature 3] Liang, Chen, et al. “Bond: Bert-assistedopen-domain named entity recognition with distant supervision.”Proceedings of the 26th ACM SIGKDD International Conference on KnowledgeDiscovery &, Data Mining. 2020.

In the technique disclosed in Reference Literature 3, a type of entityis classified with use of a classification model. The feature vectorgeneration section 12 may use this technique and use, as a featurevector, a vector which is input into a classification model.

(Feature Vector Generation Sentence)

A feature vector generation sentence is a sentence which has beenselected from a sentence set and in which one of a plurality of entitiesof interest occurs. In a case where entities e1 and e2 are entities ofinterest among entities which occur in a sentence set, the sentence setincludes m1 feature vector generation sentences in which the entity e1of interest occurs. Moreover, the sentence set includes m2 featurevector generation sentences in which the entity e2 of interest occurs.Hereinafter, a reference sign S(ek)j is given to each of mk (k=1 or 2)feature vector generation sentences. Note that mk is an integer of 1 ormore, and j is an integer of 1 or more and mk or less.

(Relation Categorization Section)

The relation categorization section 13 categorizes a relation between aplurality of entities of interest with use of a relation vector whichhas been generated by the relation vector generation section 11 andfeature vectors which have been generated by the feature vectorgeneration section 12.

<Flow of Information Processing Method>

The following description will discuss a flow of an informationprocessing method S1 that is carried out by the information processingapparatus 1, with reference to FIG. 2 . FIG. 2 is a flowchartillustrating the flow of the information processing method S1. Asillustrated in FIG. 2 , the information processing method S1 includessteps S11 through S13. Hereinafter, it is assumed that entities e1 ande2 are entities of interest among entities which occur in a sentenceset.

(Step S11)

In step S11, the relation vector generation section 11 generates arelation vector V(e1,e2) that represents a relation between the entitiese1 and e2 of interest from at least one relation vector generationsentence S(e1,e2) which has been selected from a sentence set.

(Step S12)

The feature vector generation section 12 carries out step S12 for each k(=1 or 2). In step S12, the feature vector generation section 12generates a feature vector V(ek) that represents a feature of an entityek of interest from at least one feature vector generation sentenceS(ek) which has been selected from the sentence set.

(Step S13)

In step S13, the relation categorization section 13 categorizes therelation between the entities e1 and e2 of interest with use of therelation vector V(e1,e2) which has been generated in step S11 and thefeature vectors V(e1) and V(e2) which have been generated in step S12.For example, the relation categorization section 13 generates one vectorbased on the relation vector V(e1,e2), the feature vector V(e1), and thefeature vector V(e2), and categorizes the relation between the entitiese1 and e2 of interest based on similarity between generated vectors. Forexample, the relation categorization section 13 may categorize therelation by generating a cluster of the generated vectors. Examples of amethod for generating the one vector include, but not limited to, amethod in which the relation vector V(e1,e2), the feature vector V(e1),and the feature vector V(e2) are concatenated. For example, it isassumed that a vector in which a relation vector V(e1,e2), a featurevector V(e1), and a feature vector V(e2) are concatenated is similar toa vector in which a relation vector V(e3,e4), a feature vector V(e3),and a feature vector V(e4) are concatenated. In this case, the relationcategorization section 13 determines that the “entities e1 and e2 ofinterest” have the same relation as the “entities e3 and e4 ofinterest”. Note that the relation categorization section 13 maydetermine whether or not vectors are similar to each other based, forexample, on whether or not an inner product of vectors or cosinesimilarity exceeds a threshold.

Effect of the Present Example Embodiment

As described above, in the present example embodiment, a relationbetween a plurality of entities of interest is categorized with use of arelation vector that has been generated from a relation vectorgeneration sentence in which the plurality of entities of interestoccur, and feature vectors that have been generated from feature vectorgeneration sentences in which the entities of interest occur. As aresult, features of a respective plurality of entities of interest areconsidered in addition to a relation between the plurality of entitiesof interest. Therefore, it is possible to more accurately categorize arelation between a plurality of entities of interest.

Second Example Embodiment

The following description will discuss a second example embodiment ofthe present invention in detail with reference to the drawings. The samereference numerals are given to constituent elements which havefunctions identical with those described in the first exampleembodiment, and descriptions as to such constituent elements are omittedas appropriate.

An information processing apparatus 1A according to the present exampleembodiment carries out, with modification, step S11 (relation vectorgeneration process) included in the information processing method S1according to the first example embodiment. In other words, theinformation processing apparatus 1A is an example aspect obtained byaltering the information processing apparatus 1 according to the firstexample embodiment so as to suitably carry out modified step S11.

<Configuration of Information Processing Apparatus>

The following description will discuss a configuration of theinformation processing apparatus 1A, with reference to FIG. 3 . FIG. 3is a block diagram illustrating the configuration of the informationprocessing apparatus 1A. As illustrated in FIG. 3 , the informationprocessing apparatus 1A is different from the information processingapparatus 1 according to the first example embodiment in that theinformation processing apparatus 1A includes a relation vectorgeneration section 11A instead of the relation vector generation section11, and further includes a relation vector generation parameter updatingsection 14A. The other configurations are similar to those of theinformation processing apparatus 1, and therefore detailed descriptionsof such configurations will not be repeated.

(Relation Vector Generation Section)

The relation vector generation section 11A generates a relation vectorfrom at least one relation vector generation sentence with use of analgorithm that includes a plurality of parameters. The relation vectorgeneration section 11A is an example configuration for realizing the“relation vector generation means” recited in claims.

(Specific Example of Algorithm)

A specific example of an algorithm that includes a plurality ofparameters can be a recurrent neural network. The recurrent neuralnetwork is a neural network in which a word vector sequence is used asinput, and vectors corresponding to respective word vectors constitutingthe word vector sequence are used as output. The recurrent neuralnetwork that is used by the relation vector generation section 11A ishereinafter referred to also as a first RNN. A plurality of parametersincluded in the first RNN are updated by the relation vector generationparameter updating section 14A.

(Relation Vector Generation Parameter Updating Section)

The relation vector generation parameter updating section 14A updatesthe plurality of parameters described above such that a degree ofsimilarity between a plurality of relation vectors increases. Theplurality of relation vectors are generated by the relation vectorgeneration section 11A from a plurality of sentences in all of which aplurality of entities of interest occur. Details of an updating processof updating a plurality of parameters and a specific example thereofwill be described later. The relation vector generation parameterupdating section 14A is an example configuration for realizing the“relation vector generation parameter updating means” recited in claims.

<Flow of Information Processing Method>

The following description will discuss a flow of an informationprocessing method that is carried out by the information processingapparatus 1A, with reference to FIG. 4 . The information processingapparatus 1A carries out, with modification, step S11 of the informationprocessing method S1 described above with reference to FIG. 2 . FIG. 4is a flowchart illustrating a detailed flow of a relation vectorgeneration process S11A, which is a variation of step S11.

(Flow of Relation Vector Generation Process)

As illustrated in FIG. 4 , the relation vector generation process S11Aincludes steps S111 through S114. The relation vector generation section11A carries out steps S111 through S113 for each of n relation vectorgeneration sentences S(e1,e2)i.

(Step S111)

In step S111, the relation vector generation section 11A generates aword vector sequence for the relation vector generation sentenceS(e1,e2)i. Specifically, the relation vector generation section 11Areplaces a word representing each of entities e1 and e2 of interest witha predetermined word vector. Moreover, the relation vector generationsection 11A replaces a word representing an entity other than theplurality of entities e1 and e2 of interest with a word vectorrepresenting that word. Thus, the relation vector generation section 11Agenerates a word vector sequence corresponding to the relation vectorgeneration sentence S(e1,e2)i. The process of this step is an example ofthe “first word vector sequence generation process” recited in claims.

(Specific Example of Step S111)

The following description will discuss a specific example in which, forexample, the relation vector generation sentence S(e1,e2)i is “I sawStar Wars by George Lucas at theater.”, words representing the entity e1of interest are “Star Wars”, and words representing the entity e2 ofinterest are “George Lucas”. The relation vector generation section 11Agenerates a word vector sequence (VI,Vsaw,V*,Vby,V**,Vat,Vtheater).Here, “VI” is a word vector representing the word “I”. “Vsaw” is a wordvector representing the word “saw”. “Vby” is a word vector representingthe word “by”. “Vat” is a word vector representing the word “at”.“Vtheater” is a word vector representing the word “theater”. “V*” and“V**” are predetermined word vectors, respectively.

(Step S112)

In step S112, the relation vector generation section 11A inputs, intothe first RNN, the word vector sequence which has been generated in stepS111. Thus, the relation vector generation section 11A generates an RNNoutput vector sequence that corresponds to the relation vectorgeneration sentence S(e1,e2)i. The first RNN is as described above. Aplurality of parameters included in the first RNN have been updated bythe relation vector generation parameter updating section 14A. Theprocess of this step is an example of the “first RNN output vectorsequence generation process” recited in claims.

(Specific Example of Step S112)

The following description will discuss a specific example of step S112which is carried out in response to the specific example of step S111.The relation vector generation section 11A generates an RNN outputvector sequence (WI,Wsaw,W*,Wby,W**,Wat,Wtheater) by inputting, into thefirst RNN, the word vector sequence (VI,Vsaw,V*,Vby,V**,Vat,Vtheater).Here, “WI” is a vector which is output in response to input of the wordvector “VI”. “Wsaw” is a vector which is output in response to input ofthe word vector “Vsaw”. “Wby” is a vector which is output in response toinput of the word vector “Vby”. “Wat” is a vector which is output inresponse to input of the word vector “Vat”. “Wtheater” is a vector whichis output in response to input of the word vector “Vtheater”. “W*” is avector which is output in response to input of the word vector “V*”.“W**” is a vector which is output in response to input of the wordvector “V**”.

(Step S113)

In step S113, the relation vector generation section 11A averages, foreach element, the vectors constituting the RNN output vector sequencewhich has been generated in step S112. Thus, the relation vectorgeneration section 11A calculates a sentence relation vector Vicorresponding to the relation vector generation sentence. The process ofthis step is an example of the “sentence relation vector calculationprocess” recited in claims.

In the present example embodiment, an example is described in which onesentence relation vector Vi is generated from one relation vectorgeneration sentence S(e1,e2)i. Note, however, that one sentence relationvector Vi may be generated from a plurality of relation vectorgeneration sentences S(e1,e2)i1, S(e1,e2)i2, and so forth.

(Specific Example of Step S113)

The following description will discuss a specific example of step S113which is carried out in response to the specific examples of steps S111and S112. The relation vector generation section 11A divides a sum ofthe seven vectors WI, Wsaw, W*, Wby, W**, Wat, and Wtheater constitutingthe RNN output vector sequence by 7, which is the number of vectors, andthus calculates a sentence relation vector Vi.

When the processes of steps S111 through S113 have been completed foreach of the n relation vector generation sentences S(e1,e2)i, therelation vector generation section 11A carries out a process of the nextstep S114.

(Step S114)

In step S114, the relation vector generation section 11A averages, foreach element, the sentence relation vectors Vi which correspond to therespective n relation vector generation sentences S(e1,e2)i and whichhave been calculated in step S113. Thus, the relation vector generationsection 11A calculates a relation vector V(e1,e2). In other words, therelation vector V(e1,e2) is calculated by dividing a sum of n sentencerelation vectors Vi by n. The process of this step is an example of the“relation vector calculation process” recited in claims.

(Specific Example of Parameter Updating Process)

The following description will discuss a specific example of an updatingprocess in which the relation vector generation parameter updatingsection 14A updates the plurality of parameters which are included inthe first RNN and which are used in step S112. This updating process iscarried out in advance before the information processing method S1 iscarried out. Note, however, that this updating process may beperiodically carried out with use of an additional sentence set.Hereinafter, in order to simplify the descriptions, it is assumed that asentence set which is used by the relation vector generation parameterupdating section 14A in the updating process is identical with asentence set which is dealt with in the information processing methodS1. Note, however, that a sentence set used in the updating process maybe partially or entirely different from a sentence set which is dealtwith in the information processing method S1.

First, the relation vector generation parameter updating section 14Acarries out processes similar to steps S111 through S113 for each of then relation vector generation sentences S(e1,e2)i in all of which theentities e1 and e2 of interest occur. Thus, the relation vectorgeneration parameter updating section 14A calculates n sentence relationvectors Vi.

In a specific example of the parameter updating process, n is an integerof 2 or more. The relation vector generation parameter updating section14A calculates a relation vector V(e1,e2)1 with use of n1 pieces amongthe n sentence relation vectors Vi. Moreover, the relation vectorgeneration parameter updating section 14A calculates a relation vectorV(e1,e2)2 with use of n2 pieces among the n sentence relation vectorsVi, the n2 pieces being different from the above n1 pieces. Here, n1 isan integer of 1 or more and n or less. Further, n2 is an integer of 1 ormore and (n−n1) or less. The relation vector generation parameterupdating section 14A updates a plurality of parameters such that therelation vectors V(e1,e2)1 and V(e1,e2)2 are similar to each other.

Specifically, the relation vector generation parameter updating section14A calculates a degree of similarity between the relation vectorsV(e1,e2)1 and V(e1,e2)2. Examples of the degree of similarity include,but not limited to, an inner product, a value obtained by multiplying adistance between vectors by a negative number, and the like. Therelation vector generation parameter updating section 14A updates theplurality of parameters included in the first RNN by a gradient methodsuch that the degree of similarity increases.

Note that the relation vector generation parameter updating section 14Amay update a plurality of parameters by carrying out the updatingprocess described above for each of a plurality of sets of “entities epand eq of interest”, instead of for one set of “entities e1 and e2 ofinterest”. Note that p and q are each an integer of 1 or more and n orless, and p≠q. The relation vector generation parameter updating section14A may repeat the above described updating process while changing oneor both of a combination of the n1 sentence relation vectors Vi and acombination of the n2 sentence relation vectors Vi so as to update aplurality of parameters.

Effect of the Present Example Embodiment

The present example embodiment makes it possible to generate a relationvector that represents a relation between a plurality of entities ofinterest more appropriately, as compared with the related techniquedisclosed in Non-Patent Literature 1. As a result, it is possible tomore accurately categorize a relation between entities of interest withuse of such a relation vector and feature vectors. The followingdescription will discuss a reason why a relation vector that isgenerated by the present example embodiment represents a relation moreappropriately.

Here, in the related technique disclosed in Non-Patent Literature 1,information indicating a syntactic structure of a sentence in which apair of entities of interest occurs is input into an algorithm thatincludes a plurality of parameters, and thus a relation vector isgenerated. Moreover, in the related technique, a plurality of parametersare updated from a plurality of relation vectors which have beengenerated from a plurality of sentences in which the pair occurs so thata syntactic structure of another sentence in which the pair occurs canbe predicted. Thus, this related technique considers a syntacticstructure of sentence in the generation process of a relation vector andthe updating process of parameters but does not consider features ofrespective entities.

In contrast, in the present example embodiment, a word vector sequenceis generated from each of a plurality of relation vector generationsentences in all of which a plurality of entities of interest occur.This word vector sequence not only includes information pertaining to asyntactic structure of sentence by the sequence of words, but alsorepresents features of entities to which the word vectors respectivelycorrespond. Here, an example of a feature of each of entities that arerepresented by a word vector sequence can be an occurrence position ofthat entity in a sentence. Another example of a feature of each ofentities that are represented by a word vector sequence can be a type ofthat entity which is inferred from word sequences in front and behind anoccurrence position. In the present example embodiment, a plurality ofrelation vectors are generated by inputting the generated word vectorsequence into an algorithm that includes a plurality of parameters. Theplurality of parameters have been updated in advance such that aplurality of relation vectors corresponding to a certain combination ofentities of interest are similar to each other. Thus, such a relationvector represents not only a relation based on a syntactic structure ofsentence, but also a relation based on features of respective entitiesof interest. Here, an example of a relation based on a feature of eachof entities of interest which is represented by a relation vector can bea relation based on an occurrence position of that entity of interest ina sentence. Another example of a relation based on a feature of each ofentities of interest which is represented by a relation vector can be arelation based on a type of that entity of interest which is inferredfrom word sequences in front and behind an occurrence position. Thus,the relation vector that is generated in the present example embodimentis generated in consideration of features of respective entities ofinterest, and therefore represents a relation between a plurality ofentities of interest more appropriately, as compared with a relationvector of the related technique in which a relation vector is generatedbased solely on a syntactic structure of sentence.

Third Example Embodiment

The following description will discuss a third example embodiment of thepresent invention in detail with reference to the drawings. The samereference numerals are given to constituent elements which havefunctions identical with those described in the first exampleembodiment, and descriptions as to such constituent elements are notrepeated.

An information processing apparatus 1B according to the present exampleembodiment carries out, with modification, step S12 (feature vectorgeneration process) included in the information processing method S1according to the first example embodiment. In other words, theinformation processing apparatus 1B is an example aspect obtained byaltering the information processing apparatus 1 according to the firstexample embodiment so as to suitably carry out modified step S12.

<Configuration of Information Processing Apparatus>

The following description will discuss a configuration of theinformation processing apparatus 1B, with reference to FIG. 5 . FIG. 5is a block diagram illustrating the configuration of the informationprocessing apparatus 1B. As illustrated in FIG. 5 , the informationprocessing apparatus 1B is different from the information processingapparatus 1 according to the first example embodiment in that theinformation processing apparatus 1B includes a feature vector generationsection 12B instead of the feature vector generation section 12, andfurther includes a feature vector generation parameter updating section15B. The other configurations are similar to those of the informationprocessing apparatus 1, and therefore detailed descriptions of suchconfigurations will not be repeated.

(Feature Vector Generation Section)

The feature vector generation section 12B generates, for each of aplurality of entities of interest, a feature vector from at least onefeature vector generation sentence with use of an algorithm thatincludes a plurality of parameters. The feature vector generationsection 12B is an example configuration for realizing the “featurevector generation means” recited in claims.

(Specific Example of Algorithm)

A specific example of an algorithm that includes a plurality ofparameters can be a recurrent neural network. The recurrent neuralnetwork is a neural network in which a word vector sequence is used asinput, and vectors corresponding to respective word vectors constitutingthe word vector sequence are used as output. The recurrent neuralnetwork that is used by the feature vector generation section 12B ishereinafter referred to also as a second RNN. A plurality of parametersincluded in the second RNN are updated by the feature vector generationparameter updating section 15B.

(Feature Vector Generation Parameter Updating Section)

The feature vector generation parameter updating section 15B updates theplurality of parameters such that a degree of similarity increasesbetween a feature vector and a word vector representing the entity ofinterest. The feature vector is generated by the feature vectorgeneration section 12B from a sentence in which an entity of interestoccurs. Specifically, the feature vector generation parameter updatingsection 15B updates the plurality of parameters such that a degree ofsimilarity increases between a sentence feature vector (described later)and a word vector representing the entity of interest. Details of anupdating process of updating a plurality of parameters and a specificexample thereof will be described later. The feature vector generationparameter updating section 15B is an example configuration for realizingthe “feature vector generation parameter updating means” recited inclaims.

<Flow of Information Processing Method>

The following description will discuss a flow of an informationprocessing method that is carried out by the information processingapparatus 1B, with reference to FIG. 6 . The information processingapparatus 1B carries out, with modification, step S12 of the informationprocessing method S1 described above with reference to FIG. 2 . FIG. 6is a flowchart illustrating a detailed flow of a feature vectorgeneration process S12B, which is a variation of step S12. Note that thefeature vector generation process S12B is carried out for each ofentities ek (k=1 or 2) of interest.

(Flow of Feature Vector Generation Process)

As illustrated in FIG. 6 , the feature vector generation process S12Bincludes steps S121 through S124. The feature vector generation section12B carries out steps S121 through S123 for each of mk feature vectorgeneration sentences S(ek)j.

(Step S121)

In step S121, the feature vector generation section 12B generates a wordvector sequence for the feature vector generation sentence S(ek)j.Specifically, the feature vector generation section 12B replaces a wordrepresenting an entity ek of interest with a predetermined word vector.Moreover, the feature vector generation section 12B replaces a wordrepresenting an entity other than the entity ek of interest with a wordvector representing that word. Thus, the feature vector generationsection 12B generates a word vector sequence corresponding to thefeature vector generation sentence S(ek)j. The process of this step isan example of the “second word vector sequence generation process”recited in claims.

(Specific Example of Step S121)

The following description will discuss a specific example in which, forexample, the feature vector generation sentence S(e1)j is “I saw StarWars by George Lucas at theater.”, and words representing the entity e1are “Star Wars”. The feature vector generation section 12B generates aword vector sequence (VI,Vsaw,V*,Vby,VGeorge Lucas,Vat,Vtheater). Here,“VGeorge” is a word vector representing the word “George”. The otherword vectors constituting the word vector sequence are as describedabove in the specific example of step S111.

(Step S122)

In step S122, the feature vector generation section 12B inputs, into thesecond RNN, the word vector sequence which has been generated in stepS121. Thus, the feature vector generation section 12B generates an RNNoutput vector sequence corresponding to the feature vector generationsentence S(ek)j. The second RNN is as described above. The plurality ofparameters included in the second RNN have been updated by the featurevector generation parameter updating section 15B. The process of thisstep is an example of the “second RNN output vector sequence generationprocess” recited in claims.

(Specific Example of Step S122)

The following description will discuss a specific example of step S122which is carried out in response to the specific example of step S121.The feature vector generation section 12B generates an RNN output vectorsequence (WI,Wsaw,W*,Wby,Wgeorge Lucas,Wat,Wtheater) by inputting theword vector sequence (VI,Vsaw,V*,Vby,VGeorge Lucas,Vat,Vtheater) intothe second RNN. Here, “Wgeorge” is a vector which is output in responseto input of the word vector “Vgeorge”. The other vectors constitutingthe RNN output vector sequence are as described above in the specificexample of step S112.

(Step S123)

In step S123, the feature vector generation section 12B sets a vectorcorresponding to the entity ek of interest among the vectorsconstituting the RNN output vector sequence generated in step S122 to bea sentence feature vector Vj corresponding to the feature vectorgeneration sentence S(ek)j.

(Specific Example of Step S123)

The following description will discuss a specific example of step S123which is carried out in response to the specific examples of steps S121and S122. The feature vector generation section 12B sets the vector “W*”corresponding to the entity e1 of interest to be a sentence featurevector Vj among the vectors constituting the RNN output vector sequence.The process of this step is an example of the “sentence feature vectorsetting process” recited in claims.

When the processes of steps S121 through S123 have been completed foreach of the mk relation vector generation sentences S(ek)j, the featurevector generation section 12B carries out a process of the next stepS124.

(Step S124)

In step S124, the feature vector generation section 12B averages, foreach element, the sentence feature vectors Vj which correspond to therespective feature vector generation sentences S(ek)j and which havebeen set in step S123. Thus, the feature vector generation section 12Bcalculates a feature vector V(ek). The process of this step is anexample of the “feature vector calculation process” recited in claims.

When k=1, the feature vector generation section 12B calculates a featurevector V(e1) that corresponds to the entity e1 of interest by carryingout the above described steps S121 through S124 for the entity e1 ofinterest. When k=2, the feature vector generation section 12B calculatesa feature vector V(e2) that corresponds to the entity e2 of interest bycarrying out the above described steps S121 through S124 for the entitye2 of interest.

(Specific Example of Parameter Updating Process)

The following description will discuss a specific example of an updatingprocess in which the feature vector generation parameter updatingsection 15B updates the plurality of parameters which are included inthe second RNN and are used in step S122. This updating process iscarried out in advance before the information processing method S1 iscarried out. Note, however, that this updating process may beperiodically carried out with use of an additional sentence set.Hereinafter, in order to simplify the descriptions, it is assumed that asentence set which is used by the feature vector generation parameterupdating section 15B in the updating process is identical with asentence set which is dealt with in the information processing methodS1. Note, however, that a sentence set used in the updating process maybe partially or entirely different from a sentence set which is dealtwith in the information processing method S1.

First, the feature vector generation parameter updating section 15Bcarries out processes similar to steps S121 through S123 for each of m1feature vector generation sentences S(e1)j in which the entity e1 ofinterest occurs. Thus, the feature vector generation parameter updatingsection 15B calculates m1 sentence feature vectors Vj. In a specificexample of the parameter updating process, m1 is an integer of 2 ormore. The feature vector generation parameter updating section 15Bupdates, for each of the m1 feature vector generation sentences S(e1)j,the plurality of parameters such that a degree of similarity increasesbetween a word vector of a word representing the entity e1 of interestand the sentence feature vector Vj. The feature vector generationparameter updating section 15B calculates m2 sentence feature vectors Vjfor the entity e2 of interest in a similar manner. The feature vectorgeneration parameter updating section 15B updates, for each of the m2feature vector generation sentences S(e2)j, the plurality of parameterssuch that a degree of similarity increases between a word vector of aword representing the entity e2 of interest and the sentence featurevector Vj.

Specifically, the feature vector generation parameter updating section15B calculates, as the degree of similarity between the sentence featurevector Vj and the word vector, for example, an inner product or a valueobtained by multiplying a distance between vectors by a negative number.Note, however, that the degree of similarity is not limited to these.The feature vector generation parameter updating section 15B updates theplurality of parameters included in the second RNN by a gradient methodsuch that the degree of similarity increases.

Effect of the Present Example Embodiment

The present example embodiment makes it possible to generate a featurevector that represents a feature of an entity of interest moreappropriately, as compared with the related technique disclosed inNon-Patent Literature 1. The reason for this is as follows.

For example, it is assumed that a sentence set includes a featureparameter generation sentence S(e1)j1 “I found movies by John Doe at atheater.” more than a feature parameter generation sentence S(e1)j2 “Ifound books by John Doe at a book store.”. Here, “movies” and “books”are words representing an entity e1 of interest. In this case, thefeature vector generation parameter updating section 15B inputs a wordvector sequence in which the word “movies” in the feature parametergeneration sentence S(e1)j1 is replaced with a word vector “V*” into thesecond RNN to obtain a sentence feature vector Vj1. Then, the featurevector generation parameter updating section 15B updates the parametersof the second RNN such that the sentence feature vector Vj1 is similarto an original word vector “Vmovies”. The feature vector generationparameter updating section 15B inputs a word vector sequence in whichthe word “books” in the feature parameter generation sentence S(e1)j2 isreplaced with a word vector “V” into the second RNN to obtain a sentencefeature vector Vj2. Then, the feature vector generation parameterupdating section 15B updates the parameters of the second RNN such thatthe sentence feature vector Vj2 is similar to an original word vector“Vbooks”. Here, the number of feature parameter generation sentencesS(e1)j1 is larger than that of S(e1)j2. Therefore, the plurality ofparameters are updated such that a feature vector V(e1) that representsa feature that “the entity e1 of interest is more likely to be found ina theater than in a book store” is output. In other words, in thefeature vector V(e1) output from the second RNN that includes theupdated plurality of parameters, information that “the entity e 1 ofinterest has a greater degree of likelihood of being a movie than abook” is embedded. Furthermore, in other words, the feature vector V(e1)is obtained by embedding information that matches a feature of theentity e1 of interest which is inferred from a relative magnitudebetween the number of S(e1)j1 and the number of S(e1)j2 in a sentenceset. Therefore, the feature vector that is generated in the presentexample embodiment represents a feature of an entity of interest moreappropriately.

Moreover, by using the feature vector which is generated in the presentexample embodiment, the present example embodiment makes it possible tocategorize a relation between a plurality of entities of interest moreaccurately, as compared with the related technique disclosed inNon-Patent Literature 1. The reason for this is as follows.

Here, in the related technique disclosed in Non-Patent Literature 1, arelation between entities is categorized based on a syntactic structurebetween words corresponding to two entities of interest. Therefore, thisrelated technique cannot distinguish a difference between modifiers (“ata theater” and “at a book store”) which do not directly correspond to arelation between two entities of interest (“some story” and “John Doe”)in the following sentence 1 and sentence 2. Therefore, this relatedtechnique cannot distinguish a relation between these two entities ofinterest in these two sentences.

-   -   Sentence 1: “I found Some Story by John Doe at a theater.”    -   Sentence 2: “I found Some Story by John Doe at a book store.”

In contrast, in the present example embodiment, a feature vector V(e1)of an entity e1 of interest that corresponds to the words “some story”is generated with use of the second RNN described above. Here, in a casewhere a sentence set that includes many sentences 1 is referred to, thefeature vector V(e1) represents a feature that “the entity e1 ofinterest is more likely to be found in a theater than in a book store”.Meanwhile, in a case where a sentence set that includes many sentences 2is referred to, the feature vector V(e1) represents a feature that “theentity e1 of interest is more likely to be found in a book store than ina theater”. Therefore, in the present example embodiment, a relationbetween “some story” and “Jone Doe” in the sentence set that includesmany sentences 1 can be categorized as “a work which has been producedby a movie director”. Moreover, in the present example embodiment, arelation between “some story” and “Jone Doe” in the sentence set thatincludes many sentences 2 can be categorized as “a book which has beenwritten by a writer”. Thus, in the present example embodiment, arelation is categorized with use of a feature vector that reflects afeature of an entity of interest in a sentence set. Therefore, thepresent example embodiment makes it possible to categorize a relationwith higher accuracy, as compared with the related technique disclosedin Non-Patent Literature.

Fourth Example Embodiment

The following description will discuss a fourth example embodiment ofthe present invention in detail with reference to the drawings. The samereference numerals are given to constituent elements which havefunctions identical with those described in the first exampleembodiment, and descriptions as to such constituent elements are notrepeated.

An information processing apparatus 1C according to the present exampleembodiment carries out, with modification, step S13 (relationcategorization process) included in the information processing method S1according to the first example embodiment. In other words, theinformation processing apparatus 1C is an example aspect obtained byaltering the information processing apparatus 1 according to the firstexample embodiment so as to suitably carry out modified step S13.

<Configuration of Information Processing Apparatus>

The following description will discuss a configuration of theinformation processing apparatus 1C, with reference to FIG. 7 . FIG. 7is a block diagram illustrating the configuration of the informationprocessing apparatus 1C. As illustrated in FIG. 7 , the informationprocessing apparatus 1C is different from the information processingapparatus 1 according to the first example embodiment in that theinformation processing apparatus 1C includes a relation categorizationsection 13C instead of the relation categorization section 13, andfurther includes a relation vector clustering section 16C and a featurevector clustering section 17C. The other configurations are similar tothose of the information processing apparatus 1, and therefore detaileddescriptions of such configurations will not be repeated.

(Relation Vector Clustering Section)

The relation vector clustering section 16C generates a cluster ofrelation vectors. A known technique such as, but not limited to, aK-Means method can be applied to the process of generating a cluster ofrelation vectors. The relation vector clustering section 16C classifiesa plurality of relation vectors to generate a plurality of clusters. Therelation vector clustering section 16C is an example configuration forrealizing the “relation vector clustering means” recited in claims.

(Feature Vector Clustering Section)

The feature vector clustering section 17C generates a cluster of featurevectors. A known technique such as, but not limited to, a K-Means methodcan be applied to the process of generating a cluster of featurevectors. The feature vector clustering section 17C classifies aplurality of feature vectors to generate a plurality of clusters. Thefeature vector clustering section 17C is an example configuration forrealizing the “feature vector clustering means” recited in claims.

(Relation Categorization Section)

The relation categorization section 13C carries out a relation vectorcategorization process, a feature vector categorization process, and acategorization result combining process. The relation vectorcategorization process is a process of categorizing a relation vectorwhich has been generated by the relation vector generation section 11.The feature vector categorization process is a process of categorizingeach of feature vectors generated by the feature vector generationsection 12. The categorization result combining process is a process ofcombining a categorization result obtained in the relation vectorcategorization process with a categorization result obtained in thefeature vector categorization process to categorize a relation between aplurality of entities of interest. The relation categorization section13C is an example configuration for realizing the “relationcategorization means” recited in claims.

<Flow of Information Processing Method>

The following description will discuss a flow of an informationprocessing method that is carried out by the information processingapparatus 1C, with reference to FIG. 8 . The information processingapparatus 1C carries out, with modification, step S13 of the informationprocessing method S1 described above with reference to FIG. 2 . FIG. 8is a flowchart illustrating a detailed flow of the relationcategorization process S13C which is a variation of step S13.

(Flow of Relation Categorization Process)

As illustrated in FIG. 8 , the relation categorization process S13Cincludes steps S131 through S133.

(Step S131)

In step S131, the relation categorization section 13C carries out therelation vector categorization process. Specifically, the relationcategorization section 13C determines whether or not a relation vectorV(e1,e2) which has been generated by the relation vector generationsection 11 belongs to any of clusters which have been generated by therelation vector clustering section 16C. Hereinafter, the determinedcluster is referred to as a relation cluster C(e1,e2). In a case where aweight is obtained for each of a plurality of clusters in the relationvector categorization process, a cluster with a largest weight isregarded as the relation cluster C(e1,e2).

(Step S132)

In step S132, the relation categorization section 13C carries out thefeature vector categorization process. Specifically, the relationcategorization section 13C determines whether or not each of featurevectors V(ek) which have been generated by the feature vector generationsection 12 belongs to any of clusters which have been generated by thefeature vector clustering section 17C. Hereinafter, the determinedcluster is referred to as a feature cluster C(ek). In a case where aweight is obtained for each of a plurality of clusters in the featurevector categorization process, a cluster with a largest weight isregarded the feature cluster C(ek).

(Step S133)

In step S133, the relation categorization section 13C carries out thecategorization result combining process. Specifically, the relationcategorization section 13C categorizes a relation between a plurality ofentities e1 and e2 of interest by combining the categorization resultsobtained in steps S131 and S132. For example, the relationcategorization section 13C may use a direct product of a relationcluster C(e1,e2), a feature cluster C(e1), and a feature cluster C(e2)as a categorization result of the relation between the plurality ofentities e1 and e2 of interest.

Effect of the Present Example Embodiment

In the present example embodiment, a relation cluster to which arelation vector corresponding to a plurality of entities of interestbelongs, and a feature cluster to which a feature vector correspondingto each of entities of interest belongs are combined, and thus arelation between the plurality of entities of interest is categorized.By separately carrying out categorization of a relation vector andcategorization of feature vectors in this manner, a dimension of vectorsto be considered is smaller, as compared with a case where these vectorsare categorized together. As a result, the categorization process of arelation becomes easier, and accuracy is improved.

Moreover, in the present example embodiment, the categorization resultcombining process is carried out, and this makes it possible tocategorize a relation between a plurality of entities of interest intodifferent types whose number is as much as a product of a total numberof relation clusters and a total number of feature clusters. Therefore,the capability to categorize a sufficient number of types of relationsis ensured even if a total number of clusters necessary in each of therelation vector categorization process and the feature vectorcategorization process is reduced. As a result, the categorizationprocess of a relation becomes easier, and accuracy is improved.

Therefore, the present example embodiment makes it possible to moreaccurately categorize a relation between a plurality of entities ofinterest.

Example

In this Example, the above described second through fourth exampleembodiments were carried out in combination, and verification wascarried out with respect to categorization of a relation between aplurality of entities of interest. That is, in this Example, acategorization result was obtained by carrying out the relation vectorgeneration process S11A, the feature vector generation process S12B, andthe relation categorization process S13C. The categorization result isreferred to as a categorization result of Example.

Comparative Example

As a comparative example, a relation between a plurality of entities ofinterest was categorized with use of a related technique, and thus acategorization result was obtained. As the related technique, Open IE5.1 was used. Open IE 5.1 is a known technique for categorizing arelation between entities based on a syntactic structure of sentence.

(Target Sentence Set)

In Example and Comparative Example, categorization of a relation wascarried out with respect to the same sentence set. The sentence setwhich was used (hereinafter, “target sentence set”) was a part of alarge-scale corpus ClueWeb12. The large-scale corpus ClueWeb12 is apublic data set obtained from the web by crawling. In Example andComparative Example, the FACC1 database was also used. The FACC1database includes annotation data indicating, for each of words insentences included in ClueWeb12, whether or not that word corresponds toany of entities registered in the online database Freebase, and whichone of the entities corresponds to that word.

(Correct Answer Data)

The following correct answer data was used in order to calculateaccuracy of categorization results in Example and Comparative Example.That is, a predicate which holds true for a certain pair of entities inFreebase was regarded as correct answer data which is a categorizationresult of a correct relation. Moreover, from among predicates registeredin Freebase, approximately 100 types of predicates were selected whichhold true for a pair of entities that frequently occurs in ClueWeb12.Then, among pairs of entities for which those predicates held true andwere registered in Freebase, pairs of entities which occurred in thetarget sentence set were regarded as pairs of entities of interest to becategorized. Note that the correct answer data was used to calculateaccuracy of the categorization results, and was not referred to in theupdating process and the categorization process in Example andComparative Example.

(Verification of Categorization Result)

The following description will discuss Example and Comparative Examplewith reference to FIG. 9 . FIG. 9 is a graph for comparing acategorization result in Example with a categorization result inComparative Example. In FIG. 9 , the horizontal axis represents arelation defined by each of predicates. The vertical axis indicatesaccuracy of a categorization result for a pair of entities of interesthaving that relation. More specifically, a degree of conformity betweena relation obtained by categorizing a pair of entities of interest byeach of Example and Comparative Example and a relation defined by eachof predicates shown in the horizontal axis was evaluated based on adegree of overlap between pairs of entities of interest which werecategorized into the respective relations. Then, the degrees ofconformity were weighted and averaged according to the number of pairsof entities of interest which had been categorized, and a value thusobtained was regarded as accuracy. As illustrated in FIG. 9 , thecategorization result in Example showed that it was possible to carryout categorization, although accuracy was lower than that in ComparativeExample, for a relation for which accuracy of the categorization resultin Comparative Example was relatively high. Further, the categorizationresult in Example showed that it was possible to carry outcategorization with higher accuracy than Comparative Example for arelation for which accuracy of the categorization result in ComparativeExample was low.

In other words, Example makes it possible to accurately categorize arelation even for a pair of entities of interest for whichcategorization of the relation is difficult in Comparative Example.Further, Example makes it possible to categorize more relations, ascompared with Comparative Example.

Fifth Example Embodiment

The following description will discuss a fifth example embodiment of thepresent invention in detail with reference to the drawings. The fifthexample embodiment is an example aspect obtained by altering the secondexample embodiment. The same reference numerals are given to constituentelements which have functions identical with those described in thesecond example embodiment, and descriptions as to such constituentelements are not repeated.

<Configuration of Information Processing Apparatus>

The following description will discuss a configuration of an informationprocessing apparatus 2, with reference to FIG. 10 . FIG. 10 is a blockdiagram illustrating the configuration of the information processingapparatus 2. As illustrated in FIG. 10 , the information processingapparatus 2 is different from the information processing apparatus 1Aaccording to the second example embodiment in that the informationprocessing apparatus 2 does not include the feature vector generationsection 12 and includes a relation categorization section 23 instead ofthe relation categorization section 13. The other configurations aresimilar to those of the information processing apparatus 1A, andtherefore detailed descriptions of such configurations will not berepeated.

(Relation Categorization Section)

The relation categorization section 23 categorizes a relation between aplurality of entities of interest with use of a relation vector whichhas been generated by the relation vector generation section 11A. Therelation categorization section 23 is an example configuration forrealizing the “relation categorization means” recited in claims.

<Flow of Information Processing Method>

The following description will discuss a flow of an informationprocessing method S2 that is carried out by the information processingapparatus 2, with reference to FIG. 11 . FIG. 11 is a flowchartillustrating the flow of the information processing method S2. Asillustrated in FIG. 11 , the information processing method S2 includessteps S21 and S22.

(Step S21)

The process of step S21 is similar to the process of step S11A accordingto the second example embodiment. Thus, the relation vector generationsection 11A generates a relation vector V(e1,e2) from at least onerelation vector generation sentence S(e1,e2) with use of an algorithmthat includes a plurality of parameters.

(Step S22)

In step S22, the relation categorization section 23 categorizes arelation between a plurality of entities e1 and e2 of interest with useof the relation vector V(e1,e2) which has been generated by the relationvector generation section 11A. For example, the relation categorizationsection 23 may categorize the relation by generating a cluster of therelation vectors V(e1,e2).

Effect of the Present Example Embodiment

The present example embodiment makes it possible to generate a relationvector that represents a relation between a plurality of entities ofinterest more appropriately, as compared with the related techniquedisclosed in Non-Patent Literature 1. The reason for this is asdescribed above in the effect of the second example embodiment. As aresult, the present example embodiment employs such a relation vector,and it is therefore possible to more accurately categorize a relationbetween a plurality of entities of interest.

[Software Implementation Example]

The functions of part of or all of the information processingapparatuses 1, 1A, 1B, 1C, and 2 can be realized by hardware such as anintegrated circuit (IC chip) or can be alternatively realized bysoftware.

In the latter case, each of the information processing apparatuses 1,1A, 1B, 1C, and 2 is realized by, for example, a computer that executesinstructions of a program that is software realizing the foregoingfunctions. FIG. 12 illustrates an example of such a computer(hereinafter, referred to as “computer C”). The computer C includes atleast one processor C1 and at least one memory C2. The memory C2 storesa program P for causing the computer C to function as the informationprocessing apparatuses 1, 1A, 1B, 1C, and 2. In the computer C, theprocessor C1 reads the program P from the memory C2 and executes theprogram P, so that the functions of the information processingapparatuses 1, 1A, 1B, 1C, and 2 are realized.

As the processor C1, for example, it is possible to use a centralprocessing unit (CPU), a graphic processing unit (GPU), a digital signalprocessor (DSP), a micro processing unit (MPU), a floating point numberprocessing unit (FPU), a physics processing unit (PPU), amicrocontroller, or a combination of these. The memory C2 can be, forexample, a flash memory, a hard disk drive (HDD), a solid state drive(SSD), or a combination of these.

Note that the computer C can further include a random access memory(RAM) in which the program P is loaded when the program P is executedand in which various kinds of data are temporarily stored. The computerC can further include a communication interface for carrying outtransmission and reception of data with other apparatuses. The computerC can further include an input-output interface for connectinginput-output apparatuses such as a keyboard, a mouse, a display and aprinter.

The program P can be stored in a non-transitory tangible storage mediumM which is readable by the computer C. The storage medium M can be, forexample, a tape, a disk, a card, a semiconductor memory, a programmablelogic circuit, or the like. The computer C can obtain the program P viathe storage medium M. The program P can be transmitted via atransmission medium. The transmission medium can be, for example, acommunications network, a broadcast wave, or the like. The computer Ccan obtain the program P also via such a transmission medium.

[Additional Remark 1]

The present invention is not limited to the foregoing exampleembodiments, but may be altered in various ways by a skilled personwithin the scope of the claims. For example, the present invention alsoencompasses, in its technical scope, any example embodiment derived byappropriately combining technical means disclosed in the foregoingexample embodiments.

[Additional Remark 2]

Some of or all of the foregoing example embodiments can also bedescribed as below. Note, however, that the present invention is notlimited to the following supplementary notes.

(Supplementary Note 1)

An information processing apparatus, including: a relation vectorgeneration means of generating a relation vector that represents arelation between a plurality of entities of interest from at least onerelation vector generation sentence which has been selected from asentence set and in which the plurality of entities of interest occur; afeature vector generation means of generating, for each of the pluralityof entities of interest, a feature vector that represents a feature ofthat entity of interest from at least one feature vector generationsentence which has been selected from the sentence set and in which thatentity of interest occurs; and a relation categorization means ofcategorizing a relation between the plurality of entities of interestwith use of a relation vector which has been generated by the relationvector generation means and feature vectors which have been generated bythe feature vector generation means.

According to the configuration, a relation between a plurality ofentities of interest is categorized with use of a relation vector thathas been generated from a relation vector generation sentence in whichthe plurality of entities of interest occur, and feature vectors thathave been generated from feature vector generation sentences in whichthe respective entities of interest occur. As a result, features ofrespective entities of interest are considered in addition to a relationbetween the plurality of entities of interest. Therefore, it is possibleto more accurately categorize a relation between the entities ofinterest.

(Supplementary Note 2)

The information processing apparatus according to supplementary note 1,in which: the relation vector generation means generates the relationvector from the at least one relation vector generation sentence withuse of an algorithm that includes a plurality of parameters; and theinformation processing apparatus further includes a relation vectorgeneration parameter updating means of updating the plurality ofparameters such that a degree of similarity increases between aplurality of relation vectors which are generated by the relation vectorgeneration means from a plurality of sentences in all of which theplurality of entities of interest occur.

According to the configuration, it is possible to generate a relationvector that represents a relation between a plurality of entities ofinterest more appropriately.

(Supplementary Note 3)

The information processing apparatus according to supplementary note 2,in which: the relation vector generation means carries out, for eachrelation vector generation sentence, (1) a first word vector sequencegeneration process of generating a word vector sequence that correspondsto that relation vector generation sentence by replacing a wordrepresenting each of the plurality of entities of interest with apredetermined word vector and by replacing a word representing an entityother than the plurality of entities of interest with a word vectorrepresenting that word, (2) a first RNN output vector sequencegeneration process of generating an RNN output vector sequence thatcorresponds to that relation vector generation sentence by inputting,into a recurrent neural network, the word vector sequence which has beengenerated in the first word vector sequence generation process, therecurrent neural network using a word vector sequence as input, andvectors corresponding to respective word vectors constituting the wordvector sequence as output, (3) a sentence relation vector calculationprocess of calculating a sentence relation vector that corresponds tothat relation vector generation sentence by averaging, for each element,vectors constituting the RNN output vector sequence which has beengenerated in the first RNN output vector sequence generation process,and (4) a relation vector calculation process of calculating therelation vector by averaging, for each element, sentence relationvectors which have been calculated in the sentence relation vectorcalculation process and which correspond to respective relation vectorgeneration sentences; and the relation vector generation parameterupdating means updates a parameter of the recurrent neural network suchthat a degree of similarity increases between a plurality of relationvectors that are generated by the relation vector generation means froma plurality of sentences in all of which a plurality of entities occur.

According to the configuration, it is possible to cause a recurrentneural network to learn so as to generate a relation vector thatrepresents a relation between a plurality of entities of interest moreappropriately.

(Supplementary Note 4)

The information processing apparatus according to any one ofsupplementary notes 1 through 3, in which: the feature vector generationmeans generates, for each of the plurality of entities of interest, thefeature vector from the at least one feature vector generation sentencewith use of an algorithm that includes a plurality of parameters; andthe information processing apparatus further includes a feature vectorgeneration parameter updating means of updating the plurality ofparameters such that a degree of similarity increases between a featurevector that is generated by the feature vector generation means from asentence in which an entity of interest occurs and a word vector thatrepresents the entity of interest.

According to the configuration, it is possible to generate featurevectors that represent features of respective entities of interest moreappropriately.

(Supplementary Note 5)

The information processing apparatus according to supplementary note 4,in which: the feature vector generation means carries out, for each ofthe plurality of entities of interest and for each feature vectorgeneration sentence, (1) a second word vector sequence generationprocess of generating a word vector sequence that corresponds to thatfeature vector generation sentence by replacing a word representing thatentity of interest with a predetermined word vector and replacing a wordrepresenting an entity other than that entity of interest with a wordvector representing the word, (2) a second RNN output vector sequencegeneration process of generating an RNN output vector sequence thatcorresponds to that feature vector generation sentence by inputting,into a recurrent neural network, the word vector sequence which has beengenerated in the second word vector sequence generation process, therecurrent neural network using a word vector sequence as input, andvectors corresponding to respective word vectors constituting that wordvector sequence as output, (3) a sentence feature vector setting processof setting, from among vectors constituting the RNN output vectorsequence generated in the second RNN output vector sequence generationprocess, a vector corresponding to that entity of interest to be asentence feature vector that corresponds to that feature vectorgeneration sentence, and (4) a feature vector calculation process ofcalculating the feature vector by averaging, for each element, sentencefeature vectors which have been set in the sentence feature vectorsetting process and which correspond to respective feature vectorgeneration sentences; the feature vector generation parameter updatingmeans updates a parameter of the recurrent neural network such that adegree of similarity increases between a sentence feature vector that isgenerated by the feature vector generation means from a sentence inwhich an entity of interest occurs and a word vector that represents theentity of interest.

According to the configuration, it is possible to cause a recurrentneural network to learn so as to generate feature vectors that representfeatures of respective entities of interest more appropriately.

(Supplementary Note 6)

The information processing apparatus according to any one ofsupplementary notes 1 through 5, in which: the relation categorizationmeans carries out a relation vector categorization process ofcategorizing a relation vector which has been generated by the relationvector generation means, a feature vector categorization process ofcategorizing each of feature vectors which have been generated by thefeature vector generation means, and a categorization result combiningprocess of categorizing a relation between the plurality of entities ofinterest by combining a categorization result obtained in the relationvector categorization process and a categorization result obtained inthe feature vector categorization process together.

According to the configuration, results of separately categorizing arelation vector and each of feature vectors are combined, and this makesit possible to further accurately categorize a relation between aplurality of entities of interest.

(Supplementary Note 7)

The information processing apparatus according to supplementary note 6,further including: a relation vector clustering means of generating acluster of relation vectors; and a feature vector clustering means ofgenerating a cluster of feature vectors, the relation categorizationmeans carrying out the relation vector categorization process bydetermining a cluster to which a relation vector generated by therelation vector generation means belongs, the cluster having beengenerated by the relation vector clustering means, and the relationcategorization means carrying out the feature vector categorizationprocess by determining a cluster to which each of feature vectorsgenerated by the feature vector generation means belongs, the clusterhaving been generated by the feature vector clustering means.

According to the configuration, it is possible to obtain acategorization result of a relation vector and a categorization resultof each of the feature vectors which are used for obtaining an ultimatecategorization result.

(Supplementary Note 8)

An information processing method, including: generating a relationvector that represents a relation between a plurality of entities ofinterest from at least one relation vector generation sentence which hasbeen selected from a sentence set and in which the plurality of entitiesof interest occur; generating, for each of the plurality of entities ofinterest, a feature vector that represents a feature of that entity ofinterest from at least one feature vector generation sentence which hasbeen selected from the sentence set and in which that entity of interestoccurs; and categorizing a relation between the plurality of entities ofinterest with use of the relation vector and the feature vectors whichhave been respectively generated for the plurality of entities ofinterest.

According to the configuration, an effect similar to that ofsupplementary note 1 is brought about.

(Supplementary Note 9)

A program for causing a computer to function as an informationprocessing apparatus, the program causing the computer to function as: arelation vector generation means of generating a relation vector thatrepresents a relation between a plurality of entities of interest fromat least one relation vector generation sentence which has been selectedfrom a sentence set and in which the plurality of entities of interestoccur; a feature vector generation means of generating, for each of theplurality of entities of interest, a feature vector that represents afeature of that entity of interest from at least one feature vectorgeneration sentence which has been selected from the sentence set and inwhich that entity of interest occurs; and a relation categorizationmeans of categorizing a relation between the plurality of entities ofinterest with use of a relation vector which has been generated by therelation vector generation means and feature vectors which have beengenerated by the feature vector generation means.

According to the configuration, an effect similar to that ofsupplementary note 1 is brought about.

(Supplementary Note 10)

An information processing apparatus, including: a relation vectorgeneration means of generating a relation vector with use of analgorithm including a plurality of parameters from at least one relationvector generation sentence which has been selected from a sentence setand in which a plurality of entities of interest occur, the relationvector representing a relation between the plurality of entities ofinterest; a relation vector generation parameter updating means ofupdating the plurality of parameters such that a degree of similarityincreases between a plurality of relation vectors that are generated bythe relation vector generation means from a plurality of sentences inall of which the plurality of entities of interest occur; and a relationcategorization means of categorizing a relation between the plurality ofentities of interest with use of a relation vector which has beengenerated by the relation vector generation means.

According to the configuration, an effect similar to that ofsupplementary note 1 is brought about.

(Supplementary Note 11)

An information processing method, including: generating a relationvector with use of an algorithm including a plurality of parameters fromat least one relation vector generation sentence which has been selectedfrom a sentence set and in which a plurality of entities of interestoccur, the relation vector representing a relation between the pluralityof entities of interest; updating the plurality of parameters such thata degree of similarity increases between a plurality of relation vectorsthat are generated from a plurality of sentences in all of which theplurality of entities of interest occur; and categorizing a relationbetween the plurality of entities of interest with use of the relationvector.

According to the configuration, an effect similar to that ofsupplementary note 1 is brought about.

(Supplementary Note 12)

A program for causing a computer to function as an informationprocessing apparatus, the program causing the computer to function as: arelation vector generation means of generating a relation vector withuse of an algorithm including a plurality of parameters from at leastone relation vector generation sentence which has been selected from asentence set and in which a plurality of entities of interest occur, therelation vector representing a relation between the plurality ofentities of interest; a relation vector generation parameter updatingmeans of updating the plurality of parameters such that a degree ofsimilarity increases between a plurality of relation vectors that aregenerated by the relation vector generation means from a plurality ofsentences in all of which the plurality of entities of interest occur;and a relation categorization means of categorizing a relation betweenthe plurality of entities of interest with use of a relation vectorwhich has been generated by the relation vector generation means.

According to the configuration, an effect similar to that ofsupplementary note 1 is brought about.

(Supplementary Note 13)

An information processing apparatus, including at least one processor,the at least one processor carrying out: a relation vector generationprocess of generating a relation vector that represents a relationbetween a plurality of entities of interest from at least one relationvector generation sentence which has been selected from a sentence setand in which the plurality of entities of interest occur; a featurevector generation process of generating, for each of the plurality ofentities of interest, a feature vector that represents a feature of thatentity of interest from at least one feature vector generation sentencewhich has been selected from the sentence set and in which that entityof interest occurs; and a relation categorization process ofcategorizing a relation between the plurality of entities of interestwith use of a relation vector which has been generated in the relationvector generation process and feature vectors which have been generatedin the feature vector generation process.

(Supplementary Note 14)

Furthermore, some of or all of the foregoing example embodiments canalso be expressed as below.

An information processing apparatus, including at least one processor,the at least one processor carrying out: a relation vector generationprocess of generating a relation vector with use of an algorithmincluding a plurality of parameters from at least one relation vectorgeneration sentence which has been selected from a sentence set and inwhich a plurality of entities of interest occur, the relation vectorrepresenting a relation between the plurality of entities of interest; arelation vector generation parameter updating process of updating theplurality of parameters such that a degree of similarity increasesbetween relation vectors that are generated in the relation vectorgeneration process from each of a plurality of sentences in all of whichthe plurality of entities of interest occur; and a relationcategorization process of categorizing a relation between the pluralityof entities of interest with use of a relation vector which has beengenerated in the relation vector generation process.

Note that the information processing apparatus according tosupplementary note 14 can further include a memory. The memory may storea program for causing the processor to carry out the relation vectorgeneration process, the feature vector generation process, and therelation categorization process. The program can be stored in acomputer-readable non-transitory tangible storage medium. The programcan be stored in a computer-readable non-transitory tangible storagemedium.

The information processing apparatus according to supplementary note 15can further include a memory. The memory may store a program for causingthe processor to carry out the relation vector generation process, therelation vector generation parameter updating process, and the relationcategorization process. The program can be stored in a computer-readablenon-transitory tangible storage medium.

REFERENCE SIGNS LIST

-   -   1, 1A, 1B, 1C, 2: Information processing apparatus    -   11, 11A: Relation vector generation section    -   12, 12B: Feature vector generation section    -   13, 13C, 23: Relation categorization section    -   14A: Relation vector generation parameter updating section    -   15B, 15B, 15B: Feature vector generation parameter updating        section    -   16C: Relation vector clustering section    -   17C: Feature vector clustering section    -   C1: Processor    -   C2: Memory

What is claimed is:
 1. An information processing apparatus, at least oneprocessor, the at least one processor carrying out: a relation vectorgeneration process of generating a relation vector that represents arelation between a plurality of entities of interest from at least onerelation vector generation sentence which has been selected from asentence set and in which the plurality of entities of interest occur; afeature vector generation process of generating, for each of theplurality of entities of interest, a feature vector that represents afeature of that entity of interest from at least one feature vectorgeneration sentence which has been selected from the sentence set and inwhich that entity of interest occurs; and a relation categorizationprocess of categorizing a relation between the plurality of entities ofinterest with use of a relation vector which has been generated in therelation vector generation process and feature vectors which have beengenerated in the feature vector generation process.
 2. The informationprocessing apparatus according to claim 1, wherein: in the relationvector generation process, the at least one processor generates therelation vector from the at least one relation vector generationsentence with use of an algorithm that includes a plurality ofparameters; and the at least one processor further carries out arelation vector generation parameter updating process of updating theplurality of parameters such that a degree of similarity increasesbetween a plurality of relation vectors which are generated in therelation vector generation process from a plurality of sentences in allof which the plurality of entities of interest occur.
 3. The informationprocessing apparatus according to claim 2, wherein: in the relationvector generation process, the at least one processor carries out, foreach relation vector generation sentence, (1) a first word vectorsequence generation process of generating a word vector sequence thatcorresponds to that relation vector generation sentence by replacing aword representing each of the plurality of entities of interest with apredetermined word vector and by replacing a word representing an entityother than the plurality of entities of interest with a word vectorrepresenting that word, (2) a first RNN output vector sequencegeneration process of generating an RNN output vector sequence thatcorresponds to that relation vector generation sentence by inputting,into a recurrent neural network, the word vector sequence which has beengenerated in the first word vector sequence generation process, therecurrent neural network using a word vector sequence as input, andvectors corresponding to respective word vectors constituting the wordvector sequence as output, (3) a sentence relation vector calculationprocess of calculating a sentence relation vector that corresponds tothat relation vector generation sentence by averaging, for each element,vectors constituting the RNN output vector sequence which has beengenerated in the first RNN output vector sequence generation process,and (4) a relation vector calculation process of calculating therelation vector by averaging, for each element, sentence relationvectors which have been calculated in the sentence relation vectorcalculation process and which correspond to respective relation vectorgeneration sentences; and in the relation vector generation parameterupdating process, the at least one processor updates a parameter of therecurrent neural network such that a degree of similarity increasesbetween a plurality of relation vectors that are generated in therelation vector generation process from a plurality of sentences in allof which a plurality of entities occur.
 4. The information processingapparatus according to claim 1, wherein: in the feature vectorgeneration process, the at least one processor generates, for each ofthe plurality of entities of interest, the feature vector from the atleast one feature vector generation sentence with use of an algorithmthat includes a plurality of parameters; and the at least one processorfurther carries out a feature vector generation parameter updatingprocess of updating the plurality of parameters such that a degree ofsimilarity increases between a feature vector that is generated in thefeature vector generation process from a sentence in which an entity ofinterest occurs and a word vector that represents the entity ofinterest.
 5. The information processing apparatus according to claim 4,wherein: in the feature vector generation process, the at least oneprocessor carries out, for each of the plurality of entities of interestand for each feature vector generation sentence, (1) a second wordvector sequence generation process of generating a word vector sequencethat corresponds to that feature vector generation sentence by replacinga word representing that entity of interest with a predetermined wordvector and replacing a word representing an entity other than thatentity of interest with a word vector representing the word, (2) asecond RNN output vector sequence generation process of generating anRNN output vector sequence that corresponds to that feature vectorgeneration sentence by inputting, into a recurrent neural network, theword vector sequence which has been generated in the second word vectorsequence generation process, the recurrent neural network using a wordvector sequence as input, and vectors corresponding to respective wordvectors constituting that word vector sequence as output, (3) a sentencefeature vector setting process of setting, from among vectorsconstituting the RNN output vector sequence generated in the second RNNoutput vector sequence generation process, a vector corresponding tothat entity of interest to be a sentence feature vector that correspondsto that feature vector generation sentence, and (4) a feature vectorcalculation process of calculating the feature vector by averaging, foreach element, sentence feature vectors which have been set in thesentence feature vector setting process and which correspond torespective feature vector generation sentences; and in the featurevector generation parameter updating process, the at least one processorupdates a parameter of the recurrent neural network such that a degreeof similarity increases between a sentence feature vector that isgenerated in the feature vector generation process from a sentence inwhich an entity of interest occurs and a word vector that represents theentity of interest.
 6. The information processing apparatus according toclaim 1, wherein: in the relation categorization process, the at leastone processor carries out a relation vector categorization process ofcategorizing a relation vector which has been generated in the relationvector generation process, a feature vector categorization process ofcategorizing each of feature vectors which have been generated in thefeature vector generation process, and a categorization result combiningprocess of categorizing a relation between the plurality of entities ofinterest by combining a categorization result obtained in the relationvector categorization process and a categorization result obtained inthe feature vector categorization process together.
 7. The informationprocessing apparatus according to claim 6, wherein: the at least oneprocessor further carries out a relation vector clustering process ofgenerating a cluster of relation vectors; the at least one processorfurther carries out a feature vector clustering process of generating acluster of feature vectors; in the relation categorization process, theat least one processor carries out the relation vector categorizationprocess by determining a cluster to which a relation vector generated inthe relation vector generation process belongs, the cluster having beengenerated in the relation vector clustering process; and in the relationcategorization process, the at least one processor carries out thefeature vector categorization process by determining a cluster to whicheach of feature vectors generated in the feature vector generationprocess belongs, the cluster having been generated in the feature vectorclustering process.
 8. An information processing method, comprising:generating a relation vector that represents a relation between aplurality of entities of interest from at least one relation vectorgeneration sentence which has been selected from a sentence set and inwhich the plurality of entities of interest occur; generating, for eachof the plurality of entities of interest, a feature vector thatrepresents a feature of that entity of interest from at least onefeature vector generation sentence which has been selected from thesentence set and in which that entity of interest occurs; andcategorizing a relation between the plurality of entities of interestwith use of the relation vector and the feature vectors which have beenrespectively generated for the plurality of entities of interest.
 9. Anon-transitory storage medium storing a program for causing a computerto function as an information processing apparatus recited in claim 1,the program causing the computer to carry out the relation vectorgeneration process, the feature vector generation process, and therelation categorization process.
 10. An information processingapparatus, comprising at least one processor, the at least one processorcarrying out: a relation vector generation process of generating arelation vector with use of an algorithm including a plurality ofparameters from at least one relation vector generation sentence whichhas been selected from a sentence set and in which a plurality ofentities of interest occur, the relation vector representing a relationbetween the plurality of entities of interest; a relation vectorgeneration parameter updating process of updating the plurality ofparameters such that a degree of similarity increases between aplurality of relation vector that are generated in the relation vectorgeneration process from a plurality of sentences in all of which theplurality of entities of interest occur; and a relation categorizationprocess of categorizing a relation between the plurality of entities ofinterest with use of a relation vector which has been generated in therelation vector generation process.
 11. (canceled)
 12. (canceled)